Unfortunately, there is no cookie-cutter answer to this question. It depends on your intents and your entities.

If you have intents that are easily confusable, you will need more training data. Accordingly, as you add more
intents, you also want to add more training examples for each intent. If you quickly write 20-30 unique expressions for
each intent, you should be good for the beginning.

The same holds true for entities. the number of training examples you will need depends on how closely related your different entity types are and how clearly
entities are distinguishable from non-entities in your use case.

There is a list containing all officialy supported languages here. Nevertheless, there are
others working on adding more languages, feel free to have a look at the github issues
section or the gitter chat.

The complete warning is: UndefinedMetricWarning:F-scoreisill-definedandbeingsetto0.0inlabelswithnopredictedsamples.
The warning is a result of a lack of training data. During the training the dataset will be splitted multiple times, if there are to few training samples for any of the intents, the splitting might result in splits that do not contain any examples for this intent.

Hence, the solution is to add more training samples. As this is only a warning, training will still succeed, but the resulting models predictions might be weak on the intents where you are lacking training data.

We’d love to help you. If you are unsure if your issue is related to your setup, you should state your problem in the gitter chat.
If you found an issue with the framework, please file a report on github issues
including all the information needed to reproduce the problem.